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\begin{document}

\title{Natural language generation by selection for task-oriented agents%\thanks{Grants or other notes
%about the article that should go on the front page should be
%placed here. General acknowledgments should be placed at the end of the article.}
}
\subtitle{Building virtual instructors that learn from multimodal interaction}

%\titlerunning{Short form of title}        % if too long for running head

\author{Luciana Benotti         \and
        Alexandre Denis %etc.
}

%\authorrunning{Short form of author list} % if too long for running head

\institute{Luciana Benotti \at
              Universidad Nacional de C\'ordoba \\
              \\
              \\
              \email{benotti@famaf.unc.edu.ar}           %  \\
%             \emph{Present address:} of F. Author  %  if needed
           \and
           Alexandre Denis \at
              second address
}

\date{Received: date / Accepted: date}
% The correct dates will be entered by the editor


\maketitle

\begin{abstract}

Virtual instructors can be used in several applications, ranging from trainers in simulated worlds to non player characters for virtual games. In this paper, we present a novel method for deploying virtual agents that learn to give natural language instructions by observing a human instructor interact with a real user. We call our method ``generation by selection''. We describe how our method can be used to create three virtual instructors by using existing multimodal corpora on three different virtual worlds. We automatize the semantic annotation of the corpora using an automated planner, and hence our method does not require manual annotation of the interaction data. We present the results of evaluating these instructors in an international shared task. 

\keywords{Coaching agents \and multimodal interaction \and Automated planning applications \and Human-agent interaction}
\end{abstract}


\section{Introduction} \label{sec:intro}
\input{introduction}

\section{Natural language generation for conversational agents} \label{sec:previous-work}
\input{previous-work}

\section{The generation by selection algorithms} \label{sec:algorithm}
\input{algorithm}

\section{A situated instruction giving task} \label{sec:corpus}
\input{corpora}

\section{A sample interaction with a virtual instructor} \label{sec:case-study}
\input{our-proposal}

\section{Evaluation} \label{sec:evaluation}
\input{evaluation}

\section{Discussion} \label{sec:discussion}
\input{discussion}

\section{Conclusions} \label{sec:conclusions}
\input{conclusions}

\begin{acknowledgements}
This work was partially supported by grants ANPCyT-PICT-2010-688, ANPCyT-PICT-2012-712 and the FP7-PEOPLE-2011-IRSES Project “Mobility between
Europe and Argentina applying Logics to Systems” (MEALS).
\end{acknowledgements}

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\appendix
\input{sample-annotation}

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